Triple
T12920873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TMT |
E309114
|
entity |
| Predicate | revaluationRatioToPreviousCurrency |
P79535
|
FINISHED |
| Object | 1 TMT = 5000 TMM |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 1 TMT = 5000 TMM | Statement: [TMT, revaluationRatioToPreviousCurrency, 1 TMT = 5000 TMM]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: revaluationRatioToPreviousCurrency Context triple: [TMT, revaluationRatioToPreviousCurrency, 1 TMT = 5000 TMM]
-
A.
revaluationRatio
chosen
Indicates the proportional change in value between two states or times, typically expressing how much something has been revalued relative to its previous value.
-
B.
previousCurrency
Indicates that one currency served as the predecessor or was replaced by another currency in a monetary system or sequence.
-
C.
replacedCurrencyRate
Indicates that one currency exchange rate has been superseded or substituted by another rate in a given context.
-
D.
replacedCurrencyAtRate
Indicates that one currency was substituted for another at a specified exchange rate.
-
E.
exchangeRateAtDollarization
Indicates the exchange rate that was in effect at the time a currency or economy underwent dollarization.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971e7f6e881908c7bb12283898c80 |
completed | April 10, 2026, 9:55 p.m. |
| PD | Predicate disambiguation | batch_69d96fa9b7708190a9e9fa30f59ff580 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:41 p.m.